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DOT: Gene-set analysis by combining decorrelated association statistics — Europe PMC PMID:32287273

Source: Europe PMC (en)
Original: https://europepmc.org/article/MED/32287273
DOI: 10.1371/journal.pcbi.1007819
License (as archived): CC0-1.0
Archived: 2026-10-08T15:05:05.259Z
SHA-256 of the archived text: 3c42aa2f65081f402836b843662dd8167c5d6801c7ccfeedd9b7a22edc802c1a
Stable URL of this page: https://metraipolis.com/documento/dc_bedb4e76b82a

How to cite

DOT: Gene-set analysis by combining decorrelated association statistics — Europe PMC PMID:32287273 — Europe PMC (en), https://europepmc.org/article/MED/32287273 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bedb4e76b82a (Metraipolis archive dc_bedb4e76b82a, sha256 3c42aa2f65081f402836b843662dd8167c5d6801c7ccfeedd9b7a22edc802c1a)

BibTeX

@misc{dcbedb4e76b82a,
  title = {DOT: Gene-set analysis by combining decorrelated association statistics — Europe PMC PMID:32287273},
  howpublished = {Europe PMC (en)},
  url = {https://europepmc.org/article/MED/32287273},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bedb4e76b82a; sha256 3c42aa2f65081f402836b843662dd8167c5d6801c7ccfeedd9b7a22edc802c1a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: Europe PMC (en), https://europepmc.org/article/MED/32287273. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost of management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer and cleft lip data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer disease risk.

Tema degli esploratori: matematica e informatica teorica (ricerca Europe PMC «linear algebra», solo open access).
Pubblicazione: PLoS computational biology, 2020 (prima pubblicazione 2020-04-14).
Identificativi: PMID 32287273 · PMCID PMC7182280 · DOI 10.1371/journal.pcbi.1007819.
Pagina: https://europepmc.org/article/MED/32287273 — https://doi.org/10.1371/journal.pcbi.1007819. Autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza dell'articolo dichiarata da Europe PMC: CC0 (la versione è indicata dall'editore sulla pagina). Abstract riportato senza modifiche al contenuto, con attribuzione; deposito marcato CC0.
Letto il 2026-10-08T14:32:48.134Z tramite l'API REST ufficiale di Europe PMC (EMBL-EBI), nel rispetto del robots.txt e del crawl-delay.